A sustainable ordering policies model with trade-credit for deteriorating items under the learning effect

Mahesh Kumar Jayaswal, A.K. Malik and Vijay Kumar

Journal of Management Analytics, 2024, vol. 11, issue 4, 631-658

Abstract: The carbon emission from the wastage of deteriorating items is very harmful to the environment. In general, it is believed that every item in a lot size is of high quality but practically it may not be possible that all products are of excellent quality. The deteriorating items may have a high deterioration rate and deteriorate as time passes. They can be managed with the help of preservation technologies, but involving high cost. Furthermore, shortages arise due to the deterioration of commodities, consequently influencing the demand for the product. The objective of the study is to examine the impact of carbon emission and the pattern of demand due to a shortage of products on the retailer’s total profit with the passage of time. Also, trade credit policies play a vital role for buyers and sellers and consequently impact overall profitability. Considering the importance of these factors, we have proposed a profit maximization model which incorporates trade-credit financing policy, carbon emissions, and the learning effect. This study examines how the learning effects impact the deteriorating items when trade-credit financing takes place. Detailed sensitivity analysis and numerical examples are presented to analyse the impact of model parameters on profit function. The application, future work, and managerial insight have been presented in this proposed study.

Date: 2024
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/23270012.2024.2420368 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:tjmaxx:v:11:y:2024:i:4:p:631-658

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/tjma20

DOI: 10.1080/23270012.2024.2420368

Access Statistics for this article

Journal of Management Analytics is currently edited by Li Xu

More articles in Journal of Management Analytics from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().